Quotes

Isaac Newton famously said

If I have seen farther than others it is because I have stood on the shoulders of giants.

Later Mathematician R. W. Hamming added

Mathematicians stand on each other’s shoulders while computer scientists stand on each other’s toes.

Finally, computer scientist Hal Abelson quipped

If I have not seen farther, it is because giants were standing on my shoulders.

(Thanks to Mark Reid for the Hamming quote.)

I just ran across a quote from Aristotle that seemed right in line with the quotes from John Tukey I posted the other day.

It is the mark of an educated man to look for precision in each class of things just so far as the nature of the subject admits.

I think Tukey and Aristotle may have gotten along well.

I believe Tukey said “There is no point in being precise when you don’t know what you’re talking about.” I’m going from memory, and that quote may not be verbatim. (I did a Google search on “john tukey quotes” and came up with maybe 20 pages that have the exact same three quotes from Tukey. I can’t imagine that 20 independent editors came up with the same three quotes. It’s not as if the man only said three memorable lines. I imagine there’s a great deal of copying going on.)

Here are a couple quotes from Tukey that Aristotle may have appreciated.

Finding the question is often more important than finding the answer.

The test of a good procedure is how well it works, not how well it is understood.

I have mixed feelings about the second quote. Sometimes you do have use things that work well even if you don’t understand why. For example, no one completely understands how anesthesia works. But Tukey was speaking in the context of statistical methods, and there I do see some virtue in using what you understand well even when something you don’t understand appears to work better. Maybe the poorly understood technique on appears to do better on a handful of examples and could fail on your data. But I believe Tukey was referring to techniques that many people have used successfully on a wide variety of problems even though the theoretical foundations haven’t been completely explored.

Mark Reid sent me a link to a couple quotes by John Tukey that I had not seen before. First,

To statisticians, hubris should mean the kind of pride that fosters and inflated idea of one’s powers and thereby keeps one from being more than marginally helpful to others. … The feeling of “Give me (or more likely even, give my assistant) the data, and I will tell you what the real answer is!” is one we must all fight against again and again, and yet again.

Also,

The data may not contain the answer. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.

Here are some more posts about John Tukey:

Approximate problems and approximate solutions
Innovation IV
Tukey tallying
How to linearize data for regression

From Mark Twain:

The secret of getting ahead is getting started. The secret of getting started is breaking your complex and overwhelming tasks into small manageable tasks, and then starting on the first one.

Signal vs Noise had this quote from Warren Buffet.

One thing to remember in economics is that you can’t do one thing in economics. There are always other effects that come out of it.

As I’ve heard someone say, the first step in learning to think like an economist is to ask “And then what?”

Here’s a quote from a recent blog post from Tom Peters:

You will be remembered in the long haul for the quality of your work, not the quantity of your work—the quantity part is just your defective ego talking—no one evaluates Picasso based on the number of paintings he churned out.

Greg Wilson pointed out an article in The Chronicle of Higher Education about scientists using Photoshop to manipulate the graphs of their results. The article has this to say about The Journal of Cell Biology.

So far the journal’s editors have identified 250 papers with questionable figures. Out of those, 25 were rejected because the editors determined the alterations affected the data’s interpretation.

This immediately raises suspicions of fraud which is, of course. However, I’m more concerned about carelessness than fraud. As Goethe once said,

…misunderstandings and neglect create more confusion in this world than trickery and malice. At any rate, the last two are certainly much less frequent. 

Even if researchers had innocent motivations for manipulating their graphs, they’ve made it impossible for someone else to reproduce their results and have cast doubts on their integrity.

From Robert A. Heinlein:

A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.

Here’s a quote from the Greek physician Galen (c. 130-210 A.D.)

All who drink of this remedy recover in a short time, except those whom it does not help, who all die. Therefore, it is obvious that it fails only in incurable cases.

Imagine a dialog between Galen and a modern statistician.

Stat: You say your new treatment is better than the previous one?

Galen: Yes.

Stat: But more people died on the new treatment.

Galen: Those patients don’t count because they were incurable. They would have died anyway.

The problem with Galen’s line of reasoning is that it is not falsifiable: no experiment could disprove it. He could call any treatment superior by claiming that evidence against it doesn’t count. Still, Galen might have been right.

Now suppose our statistician has a long talk with Galen and tells him about modern statistical technique.

Galen: Can’t you look back at my notes and see whether there was something different about the patients who didn’t respond to the new treatment? There’s got to be some explanation. Maybe my new treatment isn’t better for everyone, but there must be a group for whom it’s better.

Stat: Well, that’s tricky business. Advocates call that “subset analysis.” Critics call it “data dredging.” The problem is that the more clever you are with generating after-the-fact explanations, the more likely you’ll come up with one that seems true but isn’t.

Galen: I’ll have to think about that one. What do you propose we do?

Stat: We’ll have to do a randomized experiment. When each patient arrives, we’ll flip a coin to decide whether to give them the old or the new treatment. That way we expect about the same number of incurable patients to receive each treatment.

Galen: But the new treatment is better. Why should I give half my patients the worse treatment?

Stat: We don’t really know that the new treatment is better. Maybe it’s not. A randomized experiment will give us more confidence one way or another.

Galen: But couldn’t we be unlucky and assign more incurable patients to the better treatment?

Stat: Yes, that’s possible. But it’s not likely we will assign too many more incurable patients to either treatment. That’s just a chance we’ll have to take.

The issues in these imaginary dialogs come up all the time. There are people who believe their treatment is superior despite evidence to the contrary. But sometimes they’re right. New treatments are often tested on patients with poor prognosis, so the complaints of receiving more incurable patients are justified. And yet until there’s some evidence that a new treatment may be at least as good as standard, it’s unethical to give that treatment to patients with better prognosis. Sometimes post-hoc analysis finds a smoking gun, and sometimes it’s data dredging. Sometimes randomized trials fail to balance on important patient characteristics. There are no simple answers. Context is critical, and dilemmas remain despite our best efforts. That’s what makes biostatistics interesting.

It’s easy to decide what you’re going to do.  The hard thing is deciding what you’re not going to do.
Michael Dell

Clutter kills WOW.
Tom Peters

Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius — and a lot of courage — to move in the opposite direction.
Albert Einstein

From A Bias for Action:

The simple fact is that being busy is easier than not.  Most managers cannot admit that a fragmented day is actually the laziest day, the day that requires the least mental discipline and the most nervous energy.  Responding to each new request, chasing an answer to the latest question, and complaining about overwhelming demands are easier than setting priorities.

From Alan Perlis:

Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it.

 See this site for a list of other epigrams from Perlis.

The book Out of Their Minds quotes Leslie Lamport on proofs:

The proofs have been carried out to an excruciating level of detail … The reader may feel that we have given long, tedious proofs of obvious assertions. However, what he has not seen are the many equally obvious assertions that we discovered to be wrong only by trying to write similarly long, tedious proofs.                   

See Lamport’s paper How to Write a Proof. See also Complementary validation.

Here’s a quote from Gene Edward Veith to chew on.

A work is beautiful to the extent that it displays at the same time both complexity and unity.

From “Acquired taste”, World Magazine, February 9/16, 2008.